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Qiang Wang

Qiang Wang has 2 papers in our library, in 2019–2026, most of them on Machine translation; often with Bei Li, Derek F. Wong, Jingbo Zhu.

2 papers in our library · 2019–2026

OpenAlex · ORCID · h-index 17 · 207 works · 1,816 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 206 times in our library · ⭐ Paper Stars ranking: #3119

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Beijing University of Posts and TelecommunicationsBeijing · China2006–2026 · 87 paperscurrent🏢China Huadian CorporationBeijing · China2026 · 1 papercurrent🎓University of VictoriaVictoria · Canada1992–2002 · 14 papers🎓Tianjin UniversityTianjin · China1989–2021 · 10 papers🎓Xi'an Jiaotong UniversityXi'an · China2006–2024 · 7 papers🎓Northeastern UniversityShenyang · China2017–2019 · 6 papers🎓Guangdong University of TechnologyGuangzhou · China2013–2019 · 5 papers🔬State Key Laboratory of Information Photonics and Optical CommunicationsBeijing2014 · 5 papers🎓Yulin Normal UniversityYulin · China2017–2023 · 4 papers🔬State Key Laboratory of Electrical Insulation and Power EquipmentXi'an2014–2020 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 29 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

🧭 Topics 1Research subjects and languages the work is about.Machine translation · 1 work

 Works by year

2020s

20261 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Learning Deep Transformer Models for Machine Translation (2019 · 810 citations)
    For Transformer-based neural machine translation, the usual way to gain quality has been to make the model wider (Transformer-Big). Simply adding encoder layers tended to make training unstable or fail outright. The authors show that depth can beat width if two problems are handled.
  2. RepoDistill: Distilling Repository Knowledge through Compression-Aware Budget Allocation and Policy Optimization (2026)
    Repository-level coding tasks require LLMs to work with long, noisy contexts that span many files. Retrieving more code often buries the relevant parts and lowers accuracy. In the authors' pilot study with function-chunking RAG, scores rose until about 30k tokens of context and then fell sharply at 100k–200k tokens…

 Co-authors here

7 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 206 times (of the 149,439 papers whose bibliographies we read); 19 citing papers with a page here →.

Updated

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